We operate at the intersection of the latest research in machine learning and practical engineering to create useful products for society. Our work always has a strong outreach/user focus and a strong connection to machine learning.

The Insights team is composed of data scientists and designers dedicated to the these main goals:

• Gaining insights from complex data using a variety of data science methods, from Natural Language Processing to Deep Learning, with a focus on improving model interpretability.

• Creating visual interfaces that display the knowledge to both technical and non-technical audiences, and researching the best ways of doing so.

The CDS Data Engineering subteam exists to provide analysis and processing support to CDS project teams, and to develop institutional knowledge in high throughput computing.

Our team strives to develop algorithmic trading strategies. In general, we want to find a portfolio of stocks and buy/sell them in such a way that we long (or buy) at a lower price, and short (or sell) at a higher one. More specifically, we are applying statistical techniques to determine what equities to trade, and machine learning techniques to determine when it is appropriate to enter or exit a position. Of course, PnL (Profit and Loss) drives the business of trading, so our goal is to essentially have an algorithm that maximizes profit while not exposing ourselves to too much risk.

We train students to think and work like data scientists so they can make meaningful contributions to data science projects. Our Training Program is a semester-long course that helps students develop an overarching understanding of data science, and our Deep Dives are shorter workshops that introduce students to the latest technologies that are shaping the world of data science.